The calibration of the hailpads upon the Greek National Hail Suppression Program, using the Classical and Inverse Regression methods

Kyriakos G. Tsitouridis
2019 Open Science Journal  
The hailpad, constructed from a plate of Styrofoam, is a simple instrument for recording hailfall. In addition to simply recording the hailfall, calibration of the instrument is required to obtain quantitative measurements of the hail. The calibration is a process leading to a calibration equation, a polynomial establishing a relationship between the diameter of a hailstone and the dent the hailstone is left on the surface of the hailpad. A hailpad network, consisted of 154 instruments, has
more » ... nstruments, has established inGreece, in the context of the Greek National Hail Suppression Program operating for the protection of the agricultural cultivations from hail damage. For the calibration of the haipads of the network the well known "Energy Matching technique" has adopted and the Inverse Regression method is applied from the beginning for the obtainment of the calibration equation. In the present study along with the Inverse Regression method hitherto applied, the Classical Regression method is examined and presented and inferential statistics are also introduced in both methods in order to establish a more stringent statistical procedure for the calibration of the hailpads. After the theoretical analysis the data from a calibration experiment were analyzed, calibration models obtained using both methods of regression, hail diameters were predicted with the two models when new observations were available and the results compared to each other. The comparison of the two models' predictions showed that the results are almost the same so there is no good reason to replace the hitherto Inverse Regression method. However, it would be good to introduce the Classical Regression method alongside the Inverse. In addition, prediction bands for both methods should be introduced giving to the results the range of the confidence interval of the predictions.
doi:10.23954/osj.v4i1.1893 fatcat:zhrt7a3cvjbn7hmvstlkzkwjz4